Retrieve all flow-to-flow dependencies within a given namespace and render them as an ASCII dependency graph using only the flow IDs. Always return the legend after the graph.
AI agents call list_namespace_dependencies to retrieve information from Kestra Python MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries and retrieves dependency information from a namespace and presents it in a rendered format. There are no side effects—it does not execute flows, modify configurations, delete data, or trigger external operations. It is a pure read operation that gathers and displays existing metadata about flow relationships.
From the tool's definition Tool name and description: 'Retrieve all flow-to-flow dependencies within a given namespace and render them as an ASCII dependency graph'.
Documented attack patterns abuse exactly the kind of access list_namespace_dependencies gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Kestra Python MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for list_namespace_dependencies:
{
"version": "1",
"default": "deny",
"tools": {
"list_namespace_dependencies": {}
}
}list_namespace_dependencies is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Retrieve all flow-to-flow dependencies within a given namespace and render them as an ASCII dependency graph using only the flow IDs. Always return the legend after the graph. It is categorised as a Read tool in the Kestra Python MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Kestra Python MCP Server MCP server in PolicyLayer and add a rule for list_namespace_dependencies: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Kestra Python MCP Server. Nothing to install.
list_namespace_dependencies is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the list_namespace_dependencies rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for list_namespace_dependencies. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
list_namespace_dependencies is provided by the Kestra Python MCP Server MCP server (kestra-io/mcp-server-python). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Kestra Python MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
39 Kestra Python MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.